Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations1048575
Missing cells46103314
Missing cells (%)91.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory384.0 MiB
Average record size in memory384.0 B

Variable types

Numeric1
Categorical8
Text10
Unsupported29

Alerts

Carrier has constant value "Aircel"Constant
Unnamed: 24 has constant value "0.9"Constant
Unnamed: 25 has constant value "0.0"Constant
Unnamed: 27 has constant value "user"Constant
Unnamed: 28 has constant value "0.9"Constant
Unnamed: 29 has constant value "0.0"Constant
Unnamed: 37 has constant value "Andhra Pradesh in"Constant
Unnamed: 45 has constant value "user"Constant
Unnamed: 46 has constant value "0.9"Constant
Unnamed: 47 has constant value "0.0"Constant
Number is highly overall correlated with Unnamed: 22High correlation
Unnamed: 22 is highly overall correlated with NumberHigh correlation
Name has 46189 (4.4%) missing valuesMissing
Gender has 992757 (94.7%) missing valuesMissing
Address has 373038 (35.6%) missing valuesMissing
JobTitle has 1022494 (97.5%) missing valuesMissing
CompanyName has 1026899 (97.9%) missing valuesMissing
Email has 735434 (70.1%) missing valuesMissing
Facebook has 1023830 (97.6%) missing valuesMissing
Twitter has 1036915 (98.9%) missing valuesMissing
Unnamed: 10 has 1048575 (100.0%) missing valuesMissing
Unnamed: 11 has 1048575 (100.0%) missing valuesMissing
Unnamed: 12 has 1048575 (100.0%) missing valuesMissing
Unnamed: 13 has 1048575 (100.0%) missing valuesMissing
Unnamed: 14 has 1048575 (100.0%) missing valuesMissing
Unnamed: 15 has 1048575 (100.0%) missing valuesMissing
Unnamed: 16 has 1048529 (> 99.9%) missing valuesMissing
Unnamed: 17 has 1048560 (> 99.9%) missing valuesMissing
Unnamed: 18 has 1048569 (> 99.9%) missing valuesMissing
Unnamed: 19 has 1048572 (> 99.9%) missing valuesMissing
Unnamed: 20 has 1048572 (> 99.9%) missing valuesMissing
Unnamed: 21 has 1048572 (> 99.9%) missing valuesMissing
Unnamed: 22 has 1048571 (> 99.9%) missing valuesMissing
Unnamed: 23 has 1048572 (> 99.9%) missing valuesMissing
Unnamed: 24 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 25 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 26 has 1048575 (100.0%) missing valuesMissing
Unnamed: 27 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 28 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 29 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 30 has 1048575 (100.0%) missing valuesMissing
Unnamed: 31 has 1048575 (100.0%) missing valuesMissing
Unnamed: 32 has 1048575 (100.0%) missing valuesMissing
Unnamed: 33 has 1048575 (100.0%) missing valuesMissing
Unnamed: 34 has 1048575 (100.0%) missing valuesMissing
Unnamed: 35 has 1048575 (100.0%) missing valuesMissing
Unnamed: 36 has 1048575 (100.0%) missing valuesMissing
Unnamed: 37 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 38 has 1048575 (100.0%) missing valuesMissing
Unnamed: 39 has 1048575 (100.0%) missing valuesMissing
Unnamed: 40 has 1048575 (100.0%) missing valuesMissing
Unnamed: 41 has 1048575 (100.0%) missing valuesMissing
Unnamed: 42 has 1048575 (100.0%) missing valuesMissing
Unnamed: 43 has 1048575 (100.0%) missing valuesMissing
Unnamed: 44 has 1048575 (100.0%) missing valuesMissing
Unnamed: 45 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 46 has 1048574 (> 99.9%) missing valuesMissing
Unnamed: 47 has 1048574 (> 99.9%) missing valuesMissing
Number has unique valuesUnique
Facebook is an unsupported type, check if it needs cleaning or further analysisUnsupported
Twitter is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 18 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 19 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 21 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 23 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 26 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 30 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 31 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 32 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 33 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 34 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 35 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 36 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 38 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 39 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 40 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 41 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 42 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 43 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 44 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-07-17 18:14:06.441835
Analysis finished2024-07-17 18:15:22.278793
Duration1 minute and 15.84 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1048575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1878237 × 1011
Minimum9.17035 × 1011
Maximum9.1986 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2024-07-17T18:15:22.444239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.17035 × 1011
5-th percentile9.1709755 × 1011
Q19.1779304 × 1011
median9.1880144 × 1011
Q39.1970047 × 1011
95-th percentile9.1985923 × 1011
Maximum9.1986 × 1011
Range2.825 × 109
Interquartile range (IQR)1.9074319 × 109

Descriptive statistics

Standard deviation1.0090748 × 109
Coefficient of variation (CV)0.001098274
Kurtosis-1.2329713
Mean9.1878237 × 1011
Median Absolute Deviation (MAD)8.9908812 × 108
Skewness-0.52259525
Sum9.6341222 × 1017
Variance1.0182319 × 1018
MonotonicityNot monotonic
2024-07-17T18:15:22.745956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.17097 × 10111
 
< 0.1%
9.196138191 × 10111
 
< 0.1%
9.196138195 × 10111
 
< 0.1%
9.196138194 × 10111
 
< 0.1%
9.196138194 × 10111
 
< 0.1%
9.196138194 × 10111
 
< 0.1%
9.196138194 × 10111
 
< 0.1%
9.196138194 × 10111
 
< 0.1%
9.196138193 × 10111
 
< 0.1%
9.196138193 × 10111
 
< 0.1%
Other values (1048565) 1048565
> 99.9%
ValueCountFrequency (%)
9.17035 × 10111
< 0.1%
9.17035 × 10111
< 0.1%
9.17035 × 10111
< 0.1%
9.170350001 × 10111
< 0.1%
9.170350001 × 10111
< 0.1%
9.170350001 × 10111
< 0.1%
9.170350008 × 10111
< 0.1%
9.170350014 × 10111
< 0.1%
9.170350015 × 10111
< 0.1%
9.170350017 × 10111
< 0.1%
ValueCountFrequency (%)
9.1986 × 10111
< 0.1%
9.1986 × 10111
< 0.1%
9.1986 × 10111
< 0.1%
9.1986 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%
9.198599999 × 10111
< 0.1%

Carrier
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
Aircel
1048575 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6291450
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAircel
2nd rowAircel
3rd rowAircel
4th rowAircel
5th rowAircel

Common Values

ValueCountFrequency (%)
Aircel 1048575
100.0%

Length

2024-07-17T18:15:23.022701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:23.464838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
aircel 1048575
100.0%

Most occurring characters

ValueCountFrequency (%)
A 1048575
16.7%
i 1048575
16.7%
r 1048575
16.7%
c 1048575
16.7%
e 1048575
16.7%
l 1048575
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6291450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1048575
16.7%
i 1048575
16.7%
r 1048575
16.7%
c 1048575
16.7%
e 1048575
16.7%
l 1048575
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6291450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1048575
16.7%
i 1048575
16.7%
r 1048575
16.7%
c 1048575
16.7%
e 1048575
16.7%
l 1048575
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6291450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1048575
16.7%
i 1048575
16.7%
r 1048575
16.7%
c 1048575
16.7%
e 1048575
16.7%
l 1048575
16.7%

Name
Text

MISSING 

Distinct626446
Distinct (%)62.5%
Missing46189
Missing (%)4.4%
Memory size8.0 MiB
2024-07-17T18:15:24.408072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1462
Median length107
Mean length11.264574
Min length1

Characters and Unicode

Total characters11291451
Distinct characters1064
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique558218 ?
Unique (%)55.7%

Sample

1st rowPuneet Ji
2nd rowJaved Cdm Mt
3rd rowSharada
4th rowAnkit Singh
5th rowLokesh M
ValueCountFrequency (%)
kumar 40085
 
2.0%
khan 17431
 
0.9%
das 12961
 
0.7%
da 12476
 
0.6%
ali 12078
 
0.6%
mohd 11772
 
0.6%
singh 11251
 
0.6%
reddy 10883
 
0.6%
10065
 
0.5%
k 9980
 
0.5%
Other values (215425) 1821561
92.4%
2024-07-17T18:15:25.525475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1717920
 
15.2%
968527
 
8.6%
i 749360
 
6.6%
n 587671
 
5.2%
h 584400
 
5.2%
r 574455
 
5.1%
e 467215
 
4.1%
u 461760
 
4.1%
m 346858
 
3.1%
d 319209
 
2.8%
Other values (1054) 4514076
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11291451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1717920
 
15.2%
968527
 
8.6%
i 749360
 
6.6%
n 587671
 
5.2%
h 584400
 
5.2%
r 574455
 
5.1%
e 467215
 
4.1%
u 461760
 
4.1%
m 346858
 
3.1%
d 319209
 
2.8%
Other values (1054) 4514076
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11291451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1717920
 
15.2%
968527
 
8.6%
i 749360
 
6.6%
n 587671
 
5.2%
h 584400
 
5.2%
r 574455
 
5.1%
e 467215
 
4.1%
u 461760
 
4.1%
m 346858
 
3.1%
d 319209
 
2.8%
Other values (1054) 4514076
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11291451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1717920
 
15.2%
968527
 
8.6%
i 749360
 
6.6%
n 587671
 
5.2%
h 584400
 
5.2%
r 574455
 
5.1%
e 467215
 
4.1%
u 461760
 
4.1%
m 346858
 
3.1%
d 319209
 
2.8%
Other values (1054) 4514076
40.0%

Gender
Text

MISSING 

Distinct534
Distinct (%)1.0%
Missing992757
Missing (%)94.7%
Memory size8.0 MiB
2024-07-17T18:15:26.164826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length53
Median length4
Mean length4.2165431
Min length1

Characters and Unicode

Total characters235359
Distinct characters146
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique490 ?
Unique (%)0.9%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE
ValueCountFrequency (%)
male 49966
89.2%
female 5161
 
9.2%
2 30
 
0.1%
29
 
0.1%
s 28
 
< 0.1%
v 20
 
< 0.1%
0 17
 
< 0.1%
i 15
 
< 0.1%
b 10
 
< 0.1%
k 10
 
< 0.1%
Other values (593) 759
 
1.4%
2024-07-17T18:15:27.156001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 60292
25.6%
M 55180
23.4%
A 55171
23.4%
L 55141
23.4%
F 5167
 
2.2%
596
 
0.3%
a 540
 
0.2%
i 222
 
0.1%
r 212
 
0.1%
e 198
 
0.1%
Other values (136) 2640
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 60292
25.6%
M 55180
23.4%
A 55171
23.4%
L 55141
23.4%
F 5167
 
2.2%
596
 
0.3%
a 540
 
0.2%
i 222
 
0.1%
r 212
 
0.1%
e 198
 
0.1%
Other values (136) 2640
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 60292
25.6%
M 55180
23.4%
A 55171
23.4%
L 55141
23.4%
F 5167
 
2.2%
596
 
0.3%
a 540
 
0.2%
i 222
 
0.1%
r 212
 
0.1%
e 198
 
0.1%
Other values (136) 2640
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 60292
25.6%
M 55180
23.4%
A 55171
23.4%
L 55141
23.4%
F 5167
 
2.2%
596
 
0.3%
a 540
 
0.2%
i 222
 
0.1%
r 212
 
0.1%
e 198
 
0.1%
Other values (136) 2640
 
1.1%

Address
Text

MISSING 

Distinct7263
Distinct (%)1.1%
Missing373038
Missing (%)35.6%
Memory size8.0 MiB
2024-07-17T18:15:27.671533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length125
Median length111
Mean length13.801275
Min length1

Characters and Unicode

Total characters9323272
Distinct characters203
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6340 ?
Unique (%)0.9%

Sample

1st rowAndhra Pradesh in
2nd rowAndhra Pradesh
3rd rowAndhra Pradesh
4th rowAndhra Pradesh
5th rowAndhra Pradesh in
ValueCountFrequency (%)
pradesh 463714
29.9%
andhra 463474
29.9%
in 384885
24.8%
bihar 162525
 
10.5%
hyderabad 14577
 
0.9%
rajasthan 12852
 
0.8%
assam 10679
 
0.7%
2117
 
0.1%
jammu 2043
 
0.1%
kashmir 2014
 
0.1%
Other values (6822) 33624
 
2.2%
2024-07-17T18:15:28.619232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1216480
13.0%
1152165
12.4%
r 1121475
12.0%
h 1113927
11.9%
d 965730
10.4%
n 876688
9.4%
i 562558
6.0%
s 503354
5.4%
e 486876
5.2%
A 476377
 
5.1%
Other values (193) 847642
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9323272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1216480
13.0%
1152165
12.4%
r 1121475
12.0%
h 1113927
11.9%
d 965730
10.4%
n 876688
9.4%
i 562558
6.0%
s 503354
5.4%
e 486876
5.2%
A 476377
 
5.1%
Other values (193) 847642
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9323272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1216480
13.0%
1152165
12.4%
r 1121475
12.0%
h 1113927
11.9%
d 965730
10.4%
n 876688
9.4%
i 562558
6.0%
s 503354
5.4%
e 486876
5.2%
A 476377
 
5.1%
Other values (193) 847642
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9323272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1216480
13.0%
1152165
12.4%
r 1121475
12.0%
h 1113927
11.9%
d 965730
10.4%
n 876688
9.4%
i 562558
6.0%
s 503354
5.4%
e 486876
5.2%
A 476377
 
5.1%
Other values (193) 847642
9.1%

JobTitle
Text

MISSING 

Distinct7061
Distinct (%)27.1%
Missing1022494
Missing (%)97.5%
Memory size8.0 MiB
2024-07-17T18:15:29.302135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2286
Median length100
Mean length9.9498485
Min length1

Characters and Unicode

Total characters259502
Distinct characters322
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5906 ?
Unique (%)22.6%

Sample

1st row500062
2nd row500027
3rd row in
4th row Mumbai
5th row in
ValueCountFrequency (%)
in 7581
18.8%
hyderabad 4072
 
10.1%
pradesh 3590
 
8.9%
andhra 3573
 
8.9%
india 2495
 
6.2%
warangal 414
 
1.0%
vijayawada 321
 
0.8%
engineer 204
 
0.5%
tirupathi 204
 
0.5%
karimnagar 183
 
0.5%
Other values (6020) 17628
43.8%
2024-07-17T18:15:30.134035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 31149
 
12.0%
30172
 
11.6%
d 20528
 
7.9%
n 19664
 
7.6%
r 18336
 
7.1%
i 16657
 
6.4%
e 14384
 
5.5%
h 9931
 
3.8%
0 8036
 
3.1%
s 7316
 
2.8%
Other values (312) 83329
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 31149
 
12.0%
30172
 
11.6%
d 20528
 
7.9%
n 19664
 
7.6%
r 18336
 
7.1%
i 16657
 
6.4%
e 14384
 
5.5%
h 9931
 
3.8%
0 8036
 
3.1%
s 7316
 
2.8%
Other values (312) 83329
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 31149
 
12.0%
30172
 
11.6%
d 20528
 
7.9%
n 19664
 
7.6%
r 18336
 
7.1%
i 16657
 
6.4%
e 14384
 
5.5%
h 9931
 
3.8%
0 8036
 
3.1%
s 7316
 
2.8%
Other values (312) 83329
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 31149
 
12.0%
30172
 
11.6%
d 20528
 
7.9%
n 19664
 
7.6%
r 18336
 
7.1%
i 16657
 
6.4%
e 14384
 
5.5%
h 9931
 
3.8%
0 8036
 
3.1%
s 7316
 
2.8%
Other values (312) 83329
32.1%

CompanyName
Text

MISSING 

Distinct8923
Distinct (%)41.2%
Missing1026899
Missing (%)97.9%
Memory size8.0 MiB
2024-07-17T18:15:30.699793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2729
Median length67
Mean length10.37327
Min length1

Characters and Unicode

Total characters224851
Distinct characters293
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7929 ?
Unique (%)36.6%

Sample

1st row Hyderabad
2nd row Hyderbad
3rd row in
4th row Hyderabad
5th rowGalaxy Stone Man
ValueCountFrequency (%)
india 2902
 
8.5%
pradesh 2688
 
7.9%
andhra 2685
 
7.9%
in 1786
 
5.2%
hyderabad 1573
 
4.6%
ltd 307
 
0.9%
252
 
0.7%
bank 214
 
0.6%
student 198
 
0.6%
pvt 180
 
0.5%
Other values (7814) 21261
62.4%
2024-07-17T18:15:31.586410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24476
 
10.9%
23323
 
10.4%
d 15056
 
6.7%
n 15024
 
6.7%
r 14499
 
6.4%
e 13251
 
5.9%
i 12779
 
5.7%
h 8316
 
3.7%
s 7930
 
3.5%
t 6703
 
3.0%
Other values (283) 83494
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24476
 
10.9%
23323
 
10.4%
d 15056
 
6.7%
n 15024
 
6.7%
r 14499
 
6.4%
e 13251
 
5.9%
i 12779
 
5.7%
h 8316
 
3.7%
s 7930
 
3.5%
t 6703
 
3.0%
Other values (283) 83494
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24476
 
10.9%
23323
 
10.4%
d 15056
 
6.7%
n 15024
 
6.7%
r 14499
 
6.4%
e 13251
 
5.9%
i 12779
 
5.7%
h 8316
 
3.7%
s 7930
 
3.5%
t 6703
 
3.0%
Other values (283) 83494
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24476
 
10.9%
23323
 
10.4%
d 15056
 
6.7%
n 15024
 
6.7%
r 14499
 
6.4%
e 13251
 
5.9%
i 12779
 
5.7%
h 8316
 
3.7%
s 7930
 
3.5%
t 6703
 
3.0%
Other values (283) 83494
37.1%

Email
Text

MISSING 

Distinct301897
Distinct (%)96.4%
Missing735434
Missing (%)70.1%
Memory size8.0 MiB
2024-07-17T18:15:32.452466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length63
Median length46
Mean length22.722713
Min length1

Characters and Unicode

Total characters7115413
Distinct characters130
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300104 ?
Unique (%)95.8%

Sample

1st rowmobilearena@mail.com
2nd rowarunshonu45@gmail.com
3rd rowmdabdur229@gmail.com
4th row in
5th rowsunilhait28@gmail.com
ValueCountFrequency (%)
in 8520
 
2.7%
pradesh 322
 
0.1%
andhra 321
 
0.1%
hyderabad 173
 
0.1%
india 71
 
< 0.1%
47
 
< 0.1%
ltd 42
 
< 0.1%
abc@gmail.com 37
 
< 0.1%
rajasthan 33
 
< 0.1%
pvt 30
 
< 0.1%
Other values (302107) 305144
97.0%
2024-07-17T18:15:33.969326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 916108
 
12.9%
m 759218
 
10.7%
i 546091
 
7.7%
o 415273
 
5.8%
l 394237
 
5.5%
. 357680
 
5.0%
g 338982
 
4.8%
c 333358
 
4.7%
@ 302385
 
4.2%
r 242651
 
3.4%
Other values (120) 2509430
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7115413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 916108
 
12.9%
m 759218
 
10.7%
i 546091
 
7.7%
o 415273
 
5.8%
l 394237
 
5.5%
. 357680
 
5.0%
g 338982
 
4.8%
c 333358
 
4.7%
@ 302385
 
4.2%
r 242651
 
3.4%
Other values (120) 2509430
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7115413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 916108
 
12.9%
m 759218
 
10.7%
i 546091
 
7.7%
o 415273
 
5.8%
l 394237
 
5.5%
. 357680
 
5.0%
g 338982
 
4.8%
c 333358
 
4.7%
@ 302385
 
4.2%
r 242651
 
3.4%
Other values (120) 2509430
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7115413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 916108
 
12.9%
m 759218
 
10.7%
i 546091
 
7.7%
o 415273
 
5.8%
l 394237
 
5.5%
. 357680
 
5.0%
g 338982
 
4.8%
c 333358
 
4.7%
@ 302385
 
4.2%
r 242651
 
3.4%
Other values (120) 2509430
35.3%

Facebook
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1023830
Missing (%)97.6%
Memory size8.0 MiB

Twitter
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1036915
Missing (%)98.9%
Memory size8.0 MiB

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 16
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048529
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 17
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048560
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 18
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048569
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 19
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048572
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 20
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing1048572
Missing (%)> 99.9%
Memory size8.0 MiB
2024-07-17T18:15:34.385838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3
Min length1

Characters and Unicode

Total characters9
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row0
2nd rowuser
3rd rowuser
ValueCountFrequency (%)
user 2
66.7%
0 1
33.3%
2024-07-17T18:15:35.204030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 2
22.2%
s 2
22.2%
e 2
22.2%
r 2
22.2%
0 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 2
22.2%
s 2
22.2%
e 2
22.2%
r 2
22.2%
0 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 2
22.2%
s 2
22.2%
e 2
22.2%
r 2
22.2%
0 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 2
22.2%
s 2
22.2%
e 2
22.2%
r 2
22.2%
0 1
11.1%

Unnamed: 21
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048572
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 22
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)75.0%
Missing1048571
Missing (%)> 99.9%
Memory size8.0 MiB
0.0
0.9
0.31240577

Length

Max length10
Median length3
Mean length4.75
Min length3

Characters and Unicode

Total characters19
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row0.0
2nd row0.0
3rd row0.9
4th row0.31240577

Common Values

ValueCountFrequency (%)
0.0 2
 
< 0.1%
0.9 1
 
< 0.1%
0.31240577 1
 
< 0.1%
(Missing) 1048571
> 99.9%

Length

2024-07-17T18:15:35.706891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:36.255870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2
50.0%
0.9 1
25.0%
0.31240577 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 7
36.8%
. 4
21.1%
7 2
 
10.5%
9 1
 
5.3%
3 1
 
5.3%
1 1
 
5.3%
2 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
36.8%
. 4
21.1%
7 2
 
10.5%
9 1
 
5.3%
3 1
 
5.3%
1 1
 
5.3%
2 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
36.8%
. 4
21.1%
7 2
 
10.5%
9 1
 
5.3%
3 1
 
5.3%
1 1
 
5.3%
2 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
36.8%
. 4
21.1%
7 2
 
10.5%
9 1
 
5.3%
3 1
 
5.3%
1 1
 
5.3%
2 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%

Unnamed: 23
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048572
Missing (%)> 99.9%
Memory size8.0 MiB

Unnamed: 24
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.9

Common Values

ValueCountFrequency (%)
0.9 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:36.745880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:37.087210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.9 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Unnamed: 25
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:37.272407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:37.482083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Unnamed: 26
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 27
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
2024-07-17T18:15:37.626035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowuser
ValueCountFrequency (%)
user 1
100.0%
2024-07-17T18:15:38.077223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Unnamed: 28
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.9

Common Values

ValueCountFrequency (%)
0.9 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:38.348347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:38.565317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.9 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Unnamed: 29
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:38.778776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:38.999742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Unnamed: 30
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 31
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 32
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 33
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 34
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 35
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 36
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 37
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
2024-07-17T18:15:39.180549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAndhra Pradesh in
ValueCountFrequency (%)
andhra 1
33.3%
pradesh 1
33.3%
in 1
33.3%
2024-07-17T18:15:39.661467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2
11.8%
d 2
11.8%
h 2
11.8%
r 2
11.8%
a 2
11.8%
2
11.8%
A 1
5.9%
P 1
5.9%
e 1
5.9%
s 1
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2
11.8%
d 2
11.8%
h 2
11.8%
r 2
11.8%
a 2
11.8%
2
11.8%
A 1
5.9%
P 1
5.9%
e 1
5.9%
s 1
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2
11.8%
d 2
11.8%
h 2
11.8%
r 2
11.8%
a 2
11.8%
2
11.8%
A 1
5.9%
P 1
5.9%
e 1
5.9%
s 1
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2
11.8%
d 2
11.8%
h 2
11.8%
r 2
11.8%
a 2
11.8%
2
11.8%
A 1
5.9%
P 1
5.9%
e 1
5.9%
s 1
5.9%

Unnamed: 38
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 39
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 40
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 41
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 42
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 43
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 44
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1048575
Missing (%)100.0%
Memory size8.0 MiB

Unnamed: 45
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
2024-07-17T18:15:39.907205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowuser
ValueCountFrequency (%)
user 1
100.0%
2024-07-17T18:15:40.558686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Unnamed: 46
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.9

Common Values

ValueCountFrequency (%)
0.9 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:40.850811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:41.066399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.9 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Unnamed: 47
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1048574
Missing (%)> 99.9%
Memory size8.0 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 1048574
> 99.9%

Length

2024-07-17T18:15:41.248310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T18:15:41.464097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Interactions

2024-07-17T18:14:59.732212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-17T18:15:41.597386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
NumberUnnamed: 22
Number1.0000.707
Unnamed: 220.7071.000

Missing values

2024-07-17T18:15:00.901613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-17T18:15:07.738827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-17T18:15:19.499466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NumberCarrierNameGenderAddressJobTitleCompanyNameEmailFacebookTwitterUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27Unnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36Unnamed: 37Unnamed: 38Unnamed: 39Unnamed: 40Unnamed: 41Unnamed: 42Unnamed: 43Unnamed: 44Unnamed: 45Unnamed: 46Unnamed: 47
09.170970e+11AircelPuneet JiNaNAndhra Pradesh inNaNNaNmobilearena@mail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19.170970e+11AircelNaNNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
29.170970e+11AircelJaved Cdm MtNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39.170970e+11AircelSharadaNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
49.170970e+11AircelAnkit SinghNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
59.170970e+11AircelLokesh MNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
69.170970e+11AircelArun ShonuNaNAndhra Pradesh inNaNNaNarunshonu45@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
79.170970e+11AircelMyNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
89.170970e+11AircelChintuNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99.170970e+11AircelDilipNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NumberCarrierNameGenderAddressJobTitleCompanyNameEmailFacebookTwitterUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27Unnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36Unnamed: 37Unnamed: 38Unnamed: 39Unnamed: 40Unnamed: 41Unnamed: 42Unnamed: 43Unnamed: 44Unnamed: 45Unnamed: 46Unnamed: 47
10485659.180833e+11AircelChote BossNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485669.180833e+11AircelIndrajit PaperNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485679.180833e+11AircelBicky Hp PumpNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485689.180833e+11AircelRahul Singh RajputNaNBihar inNaNNaNrahulsingh808330@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485699.180833e+11AircelNiwesh SirNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485709.180833e+11AircelRavi KumarNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485719.180833e+11AircelDheeraj KumarNaNBihar inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485729.180833e+11AircelPenter Naya ChilwaniaNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485739.180833e+11AircelSatishNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10485749.180833e+11AircelTipuNaNBiharNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN